SYSTEM AND METHOD FOR DETECTING NON-MEANINGFUL MOTION

• US 20160231109A1
• Filed: 02/09/2015
• Published: 08/11/2016
• Est. Priority Date: 02/09/2015
• Status: Active Grant

• Current Assignee: Invensense Incorporated (TDK Corporation)
• Original Assignee: Invensense Incorporated (TDK Corporation)
• Inventors: Chang, Hsui-Wen, Georgy, Jacques
• Granted Patent: US 10,830,606 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: G01P 13/00 Indicating or recording presence, absence, or direction, of movement electric switches H01H; counting moving objects G06M7/00
• CPC Class Codes: G06V 40/25 Recognition of walking or running movements, e.g. gait recognition

Abstract

This disclosure relates to detecting non-meaningful motion with a portable device. In one aspect, a suitable method includes detecting motion with a portable device by obtaining inertial sensor data representing motion of the portable device, wherein the inertial sensor data includes accelerometer data and gyroscope data, processing the accelerometer data, processing the gyroscope data and identifying non-meaningful motion of the portable device based, at least in part, on the processed accelerometer data and the processed gyroscope data.

Claims

1. A method for detecting motion with a portable device comprising:

obtaining inertial sensor data representing motion of the portable device, wherein the inertial sensor data includes accelerometer data and gyroscope data;

processing the accelerometer data;

processing the gyroscope data; and

identifying non-meaningful motion of the portable device based, at least in part, on the processed accelerometer data and the processed gyroscope data.

2. The method of claim 1, wherein processing the accelerometer data comprises leveling the accelerometer data to obtain a vertical component.

3. The method of claim 2, wherein processing the accelerometer data further comprises subtracting gravity from the obtained vertical component.

4. The method of claim 1, wherein processing the gyroscope data comprises determining a magnitude for the gyroscope data.

5. The method of claim 4, wherein processing the gyroscope data further comprises leveling the gyroscope data to obtain a horizontal component.

6. The method of claim 1, wherein identifying non-meaningful motion is based at least in part on a statistical analysis.

7. The method of claim 1, wherein identifying non-meaningful motion is based at least in part on a frequency analysis.

8. The method of claim 1, wherein identifying non-meaningful motion comprises matching at least one of the accelerometer data and gyroscope data to a known pattern.

9. The method of claim 1, wherein identifying non-meaningful motion further comprises adjusting a non-meaningful motion identification based, at least in part, on a history of non-meaningful motion identification for the portable device.

10. The method of claim 1, further comprising classifying motion of the portable device, wherein identifying non-meaningful motion of the portable device is also based on a use case corresponding to the classified motion.

11. The method of claim 1, further comprising deriving a navigation solution for the portable device from the inertial sensor data.

12. The method of claim 1, wherein processing the accelerometer data comprises leveling the accelerometer data using pitch and roll angles from the derived navigation solution to obtain a vertical component.

13. The method of claim 11, wherein processing the gyroscope data comprises leveling the gyroscope data using pitch and roll angles from the derived navigation solution to obtain a horizontal component.

14. The method of claim 1, wherein identifying a non-meaningful motion is based, at least in part, on a vertical velocity of the portable device.

15. The method of claim 11, wherein identifying a non-meaningful motion is based, at least in part, on a vertical velocity determined from the navigation solution.

16. The method of claim 1, wherein identifying a non-meaningful motion is based, at least in part, on a horizontal velocity of the portable device.

17. The method of claim 11 wherein identifying a non-meaningful motion is based, at least in part, on a horizontal velocity determined from the navigation solution.

18. The method of claim 1, further comprising performing a filtering operation.

19. The method of claim 1, further comprising characterizing periodic motion of a user of the portable device and assessing the characterization based, at least in part, on the identification of non-meaningful motion.

20. The method of claim 19, wherein characterizing the periodic motion comprises detecting at least one possible periodic cycle and wherein assessing the characterization comprises overruling detection of the at least one possible periodic cycle.

21. The method of claim 20, further comprising estimating a distance traveled by the portable device based, at least in part, on the assessed characterization.

22. The method of claim 20, further comprising updating a navigational solution for the portable device based, at least in part, on the assessed characterization.

23. The method of claim 19, wherein the periodic motion is on foot motion.

24. The method of claim 20, wherein the periodic motion is on foot motion and the possible periodic cycle is a step.

25. The method of claim 1, further comprising characterizing conveyed motion of a user of the portable device and assessing the characterization based, at least in part, on the identification of non-meaningful motion.

26. The method of claim 25, wherein the conveyed motion of the user comprises transport in a vessel.

27. A portable device for detecting motion comprising:

an inertial sensor integrated with the portable device configured to output accelerometer data and gyroscope data; and

a non-meaningful motion detector configured to;

process the accelerometer data;

process the gyroscope data; and

identify non-meaningful motion of the portable device based, at least in part, on the processed accelerometer data and the processed gyroscope data.

28. The device of claim 27, further comprising a step detector configured to characterize on foot motion of a user.

29. The device of claim 27, further comprising a navigational module configured to determine a navigation solution for the portable device.

30. The device of claim 28, further comprising a navigational module configured to determine a navigation solution for the portable device based, at least in part, on output from the step detector.

31. The device of claim 30, wherein the output of the step detector is assessed using the identification of non-meaningful motion.

32. The device of claim 28, wherein the output of the step detector is assessed using the identification of non-meaningful motion.

33. The device of claim 27, further comprising a periodic motion detector configured to characterize periodic motion of a user.

34. The device of claim 33, further comprising a navigational module configured to determine a navigation solution for the portable device based, at least in part, on output from the periodic motion detector.

35. The device of claim 34, wherein the output of the periodic motion detector is assessed using the identification of non-meaningful motion.

36. The device of claim 33, wherein the output of the periodic motion detector is assessed using the identification of non-meaningful motion.

37. The device of claim 27, further comprising a conveyed motion detector configured to characterize conveyed motion of a user.

38. The device of claim 37, further comprising a navigational module configured to determine a navigation solution for the portable device based, at least in part, on output from the conveyed motion detector.

39. The device of claim 38, wherein the output of the conveyed motion detector is assessed using the identification of non-meaningful motion.

40. The device of claim 37, wherein the output of the conveyed motion detector is assessed using the identification of non-meaningful motion.

41. The device of claim 27, wherein the inertial sensor is implemented as a Micro Electro Mechanical System (MEMS).